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EXPEDITE (OutboundAI)

StatusPRs WelcomeGDPR CompliantArchitectureTech Stack

EXPEDITE is an autonomous AI agent designed for B2B sales and recruiting outreach. It drastically reduces manual prospect research by utilizing advanced LLM pipelines (LangGraph) to find verified leads, extract insights, and draft hyper-personalized outreach emails.

Built for scale, speed, and real-world ROI.

Methodology

EXPEDITE operates on an Evidence-First Pipeline. Unlike traditional scraping wrappers, EXPEDITE leverages agentic orchestration to ensure every prospect is verified and every drafted email contains personalized, highly relevant context.

  1. Intent & Location Scoping: The user defines an objective (e.g., "Find Series A fintechs") and an optional location (e.g., "San Francisco"). The agent translates this into targeted API queries.
  2. Parallel Agent Execution: Using a LangGraph state machine (ScoutAgent), the system orchestrates sub-tasks. It searches Hunter.io and Apollo for domain contacts, pulling recent news and company intelligence concurrently.
  3. Data Verification (Proof Ledger): Every lead is subjected to a deliverability check (MX records, SMTP checks) and recorded in a transparent Proof Ledger.
  4. Contextual Drafting: Instead of generic templates, the LLM uses the gathered company intelligence and location context to write personalized drafts designed to cut through the noise.
  5. ROI Analytics: The platform strictly tracks output, actively visualizing the hours saved, leads found, and emails drafted on the main dashboard.

Architecture & Flow Diagram

The application is built on a split architecture: a lightweight React/Vite frontend and a robust, async-first FastAPI backend.

graph TD
%% Frontend Components
subgraph Frontend ["Frontend (React + Vite)"]
UI[Launchpad UI] --> ApiClient[API Client]
Dashboard[ROI Dashboard] --> ApiClient
end
%% Backend Components
subgraph Backend ["Backend (FastAPI)"]
Router[Missions Router]
Agent[ScoutAgent]
LLM[LLM Service]
Integrations[Integration Layer]
ApiClient -->|POST /missions| Router
Router --> Agent
Agent <--> LLM
Agent --> Integrations
end
%% External Services
subgraph External ["External Services"]
Hunter[Hunter.io API]
Apollo[Apollo API]
WebScraper[Firecrawl / Web]
Integrations --> Hunter
Integrations --> Apollo
Integrations --> WebScraper
end
%% Database
subgraph DB ["Database"]
Mongo[(MongoDB)]
Router --> Mongo
Agent --> Mongo
end
Loading

Getting Started

Prerequisites

  • Python 3.12+ (managed via uv)
  • Node.js v18+
  • MongoDB instance (Cloud or Local)

1. Backend Setup

Navigate to the backend directory and set up the environment:

cd backend
# Install dependencies using uv
uv sync
# Configure your environment
cp .env.example .env
# Fill in OPENAI_API_KEY, HUNTER_API_KEY, MONGODB_URI, etc.# Run the FastAPI server
uv run uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload

2. Frontend Setup

Navigate to the frontend directory:

cd frontend
# Install dependencies
npm install
# Run the development server
npm run dev

Security & Privacy (Trust Center)

EXPEDITE was built with enterprise-grade security in mind:

  • GDPR Compliant: Designed with data minimization principles.
  • Isolated Execution: User data is processed in isolated execution environments.
  • Zero Raw Passwords: Strict enforcement against storing raw passwords; robust auth via Clerk.

Contributing

We love open-source contributions! If you're interested in helping us build EXPEDITE, please check out our Contributing Guidelines for details on how to set up your local environment, navigate the codebase, and submit Pull Requests.

Key Features

  • ROI Analytics Dashboard: Real-time visibility into manual hours saved and leads verified.
  • Location-Specific Targeting: Hyper-local prospect searching directly from the Launchpad.
  • Intelligent Caching: Heavily cached external API calls to minimize latency and costs.
  • Lightweight & Fast: Bloat-free frontend design prioritizing UX and speed.

About

Your personal AI agent for sales,Jobs and other buisnesses made for zenith hackathon

expediteai.vercel.app/

Topics

langchainlanggraphtypescript

Resources

Readme

Contributing

Contributing
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

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GitHub - BEASTSHRIRAM/OutboundAI: Your personal AI agent for sales,Jobs and other buisnesses made for zenith hackathon · GitHub
Skip to content
BranchesTags
Repository files navigation
EXPEDITE (OutboundAI)
StatusPRs WelcomeGDPR CompliantArchitectureTech Stack
EXPEDITE is an autonomous AI agent designed for B2B sales and recruiting outreach. It drastically reduces manual prospect research by utilizing advanced LLM pipelines (LangGraph) to find verified leads, extract insights, and draft hyper-personalized outreach emails.
Built for scale, speed, and real-world ROI.
Methodology
EXPEDITE operates on an Evidence-First Pipeline. Unlike traditional scraping wrappers, EXPEDITE leverages agentic orchestration to ensure every prospect is verified and every drafted email contains personalized, highly relevant context.
  1. Intent & Location Scoping: The user defines an objective (e.g., "Find Series A fintechs") and an optional location (e.g., "San Francisco"). The agent translates this into targeted API queries.
  2. Parallel Agent Execution: Using a LangGraph state machine (ScoutAgent), the system orchestrates sub-tasks. It searches Hunter.io and Apollo for domain contacts, pulling recent news and company intelligence concurrently.
  3. Data Verification (Proof Ledger): Every lead is subjected to a deliverability check (MX records, SMTP checks) and recorded in a transparent Proof Ledger.
  4. Contextual Drafting: Instead of generic templates, the LLM uses the gathered company intelligence and location context to write personalized drafts designed to cut through the noise.
  5. ROI Analytics: The platform strictly tracks output, actively visualizing the hours saved, leads found, and emails drafted on the main dashboard.
Architecture & Flow Diagram
The application is built on a split architecture: a lightweight React/Vite frontend and a robust, async-first FastAPI backend.
graph TD
%% Frontend Components
subgraph Frontend ["Frontend (React + Vite)"]
UI[Launchpad UI] --> ApiClient[API Client]
Dashboard[ROI Dashboard] --> ApiClient
end
%% Backend Components
subgraph Backend ["Backend (FastAPI)"]
Router[Missions Router]
Agent[ScoutAgent]
LLM[LLM Service]
Integrations[Integration Layer]
ApiClient -->|POST /missions| Router
Router --> Agent
Agent <--> LLM
Agent --> Integrations
end
%% External Services
subgraph External ["External Services"]
Hunter[Hunter.io API]
Apollo[Apollo API]
WebScraper[Firecrawl / Web]
Integrations --> Hunter
Integrations --> Apollo
Integrations --> WebScraper
end
%% Database
subgraph DB ["Database"]
Mongo[(MongoDB)]
Router --> Mongo
Agent --> Mongo
end

Loading
Getting Started
Prerequisites
  • Python 3.12+ (managed via uv)
  • Node.js v18+
  • MongoDB instance (Cloud or Local)
1. Backend Setup
Navigate to the backend directory and set up the environment:
cd backend
# Install dependencies using uv
uv sync
# Configure your environment
cp .env.example .env
# Fill in OPENAI_API_KEY, HUNTER_API_KEY, MONGODB_URI, etc.# Run the FastAPI server
uv run uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
2. Frontend Setup
Navigate to the frontend directory:
cd frontend
# Install dependencies
npm install
# Run the development server
npm run dev
Security & Privacy (Trust Center)
EXPEDITE was built with enterprise-grade security in mind:
  • GDPR Compliant: Designed with data minimization principles.
  • Isolated Execution: User data is processed in isolated execution environments.
  • Zero Raw Passwords: Strict enforcement against storing raw passwords; robust auth via Clerk.
Contributing
We love open-source contributions! If you're interested in helping us build EXPEDITE, please check out our Contributing Guidelines for details on how to set up your local environment, navigate the codebase, and submit Pull Requests.
Key Features
  • ROI Analytics Dashboard: Real-time visibility into manual hours saved and leads verified.
  • Location-Specific Targeting: Hyper-local prospect searching directly from the Launchpad.
  • Intelligent Caching: Heavily cached external API calls to minimize latency and costs.
  • Lightweight & Fast: Bloat-free frontend design prioritizing UX and speed.
About
Your personal AI agent for sales,Jobs and other buisnesses made for zenith hackathon
expediteai.vercel.app/
Topics
langchainlanggraphtypescript
Resources
Readme
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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var __re = new RegExp('^' + ".*" + '
GitHub - BEASTSHRIRAM/OutboundAI: Your personal AI agent for sales,Jobs and other buisnesses made for zenith hackathon · GitHub
Skip to content
BranchesTags
Repository files navigation
EXPEDITE (OutboundAI)
StatusPRs WelcomeGDPR CompliantArchitectureTech Stack
EXPEDITE is an autonomous AI agent designed for B2B sales and recruiting outreach. It drastically reduces manual prospect research by utilizing advanced LLM pipelines (LangGraph) to find verified leads, extract insights, and draft hyper-personalized outreach emails.
Built for scale, speed, and real-world ROI.
Methodology
EXPEDITE operates on an Evidence-First Pipeline. Unlike traditional scraping wrappers, EXPEDITE leverages agentic orchestration to ensure every prospect is verified and every drafted email contains personalized, highly relevant context.
  1. Intent & Location Scoping: The user defines an objective (e.g., "Find Series A fintechs") and an optional location (e.g., "San Francisco"). The agent translates this into targeted API queries.
  2. Parallel Agent Execution: Using a LangGraph state machine (ScoutAgent), the system orchestrates sub-tasks. It searches Hunter.io and Apollo for domain contacts, pulling recent news and company intelligence concurrently.
  3. Data Verification (Proof Ledger): Every lead is subjected to a deliverability check (MX records, SMTP checks) and recorded in a transparent Proof Ledger.
  4. Contextual Drafting: Instead of generic templates, the LLM uses the gathered company intelligence and location context to write personalized drafts designed to cut through the noise.
  5. ROI Analytics: The platform strictly tracks output, actively visualizing the hours saved, leads found, and emails drafted on the main dashboard.
Architecture & Flow Diagram
The application is built on a split architecture: a lightweight React/Vite frontend and a robust, async-first FastAPI backend.
graph TD
%% Frontend Components
subgraph Frontend ["Frontend (React + Vite)"]
UI[Launchpad UI] --> ApiClient[API Client]
Dashboard[ROI Dashboard] --> ApiClient
end
%% Backend Components
subgraph Backend ["Backend (FastAPI)"]
Router[Missions Router]
Agent[ScoutAgent]
LLM[LLM Service]
Integrations[Integration Layer]
ApiClient -->|POST /missions| Router
Router --> Agent
Agent <--> LLM
Agent --> Integrations
end
%% External Services
subgraph External ["External Services"]
Hunter[Hunter.io API]
Apollo[Apollo API]
WebScraper[Firecrawl / Web]
Integrations --> Hunter
Integrations --> Apollo
Integrations --> WebScraper
end
%% Database
subgraph DB ["Database"]
Mongo[(MongoDB)]
Router --> Mongo
Agent --> Mongo
end

Loading
Getting Started
Prerequisites
  • Python 3.12+ (managed via uv)
  • Node.js v18+
  • MongoDB instance (Cloud or Local)
1. Backend Setup
Navigate to the backend directory and set up the environment:
cd backend
# Install dependencies using uv
uv sync
# Configure your environment
cp .env.example .env
# Fill in OPENAI_API_KEY, HUNTER_API_KEY, MONGODB_URI, etc.# Run the FastAPI server
uv run uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
2. Frontend Setup
Navigate to the frontend directory:
cd frontend
# Install dependencies
npm install
# Run the development server
npm run dev
Security & Privacy (Trust Center)
EXPEDITE was built with enterprise-grade security in mind:
  • GDPR Compliant: Designed with data minimization principles.
  • Isolated Execution: User data is processed in isolated execution environments.
  • Zero Raw Passwords: Strict enforcement against storing raw passwords; robust auth via Clerk.
Contributing
We love open-source contributions! If you're interested in helping us build EXPEDITE, please check out our Contributing Guidelines for details on how to set up your local environment, navigate the codebase, and submit Pull Requests.
Key Features
  • ROI Analytics Dashboard: Real-time visibility into manual hours saved and leads verified.
  • Location-Specific Targeting: Hyper-local prospect searching directly from the Launchpad.
  • Intelligent Caching: Heavily cached external API calls to minimize latency and costs.
  • Lightweight & Fast: Bloat-free frontend design prioritizing UX and speed.
About
Your personal AI agent for sales,Jobs and other buisnesses made for zenith hackathon
expediteai.vercel.app/
Topics
langchainlanggraphtypescript
Resources
Readme
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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mutations.forEach(function(m) {
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} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - BEASTSHRIRAM/OutboundAI: Your personal AI agent for sales,Jobs and other buisnesses made for zenith hackathon · GitHub
Skip to content
BranchesTags
Repository files navigation
EXPEDITE (OutboundAI)
StatusPRs WelcomeGDPR CompliantArchitectureTech Stack
EXPEDITE is an autonomous AI agent designed for B2B sales and recruiting outreach. It drastically reduces manual prospect research by utilizing advanced LLM pipelines (LangGraph) to find verified leads, extract insights, and draft hyper-personalized outreach emails.
Built for scale, speed, and real-world ROI.
Methodology
EXPEDITE operates on an Evidence-First Pipeline. Unlike traditional scraping wrappers, EXPEDITE leverages agentic orchestration to ensure every prospect is verified and every drafted email contains personalized, highly relevant context.
  1. Intent & Location Scoping: The user defines an objective (e.g., "Find Series A fintechs") and an optional location (e.g., "San Francisco"). The agent translates this into targeted API queries.
  2. Parallel Agent Execution: Using a LangGraph state machine (ScoutAgent), the system orchestrates sub-tasks. It searches Hunter.io and Apollo for domain contacts, pulling recent news and company intelligence concurrently.
  3. Data Verification (Proof Ledger): Every lead is subjected to a deliverability check (MX records, SMTP checks) and recorded in a transparent Proof Ledger.
  4. Contextual Drafting: Instead of generic templates, the LLM uses the gathered company intelligence and location context to write personalized drafts designed to cut through the noise.
  5. ROI Analytics: The platform strictly tracks output, actively visualizing the hours saved, leads found, and emails drafted on the main dashboard.
Architecture & Flow Diagram
The application is built on a split architecture: a lightweight React/Vite frontend and a robust, async-first FastAPI backend.
graph TD
%% Frontend Components
subgraph Frontend ["Frontend (React + Vite)"]
UI[Launchpad UI] --> ApiClient[API Client]
Dashboard[ROI Dashboard] --> ApiClient
end
%% Backend Components
subgraph Backend ["Backend (FastAPI)"]
Router[Missions Router]
Agent[ScoutAgent]
LLM[LLM Service]
Integrations[Integration Layer]
ApiClient -->|POST /missions| Router
Router --> Agent
Agent <--> LLM
Agent --> Integrations
end
%% External Services
subgraph External ["External Services"]
Hunter[Hunter.io API]
Apollo[Apollo API]
WebScraper[Firecrawl / Web]
Integrations --> Hunter
Integrations --> Apollo
Integrations --> WebScraper
end
%% Database
subgraph DB ["Database"]
Mongo[(MongoDB)]
Router --> Mongo
Agent --> Mongo
end

Loading
Getting Started
Prerequisites
  • Python 3.12+ (managed via uv)
  • Node.js v18+
  • MongoDB instance (Cloud or Local)
1. Backend Setup
Navigate to the backend directory and set up the environment:
cd backend
# Install dependencies using uv
uv sync
# Configure your environment
cp .env.example .env
# Fill in OPENAI_API_KEY, HUNTER_API_KEY, MONGODB_URI, etc.# Run the FastAPI server
uv run uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
2. Frontend Setup
Navigate to the frontend directory:
cd frontend
# Install dependencies
npm install
# Run the development server
npm run dev
Security & Privacy (Trust Center)
EXPEDITE was built with enterprise-grade security in mind:
  • GDPR Compliant: Designed with data minimization principles.
  • Isolated Execution: User data is processed in isolated execution environments.
  • Zero Raw Passwords: Strict enforcement against storing raw passwords; robust auth via Clerk.
Contributing
We love open-source contributions! If you're interested in helping us build EXPEDITE, please check out our Contributing Guidelines for details on how to set up your local environment, navigate the codebase, and submit Pull Requests.
Key Features
  • ROI Analytics Dashboard: Real-time visibility into manual hours saved and leads verified.
  • Location-Specific Targeting: Hyper-local prospect searching directly from the Launchpad.
  • Intelligent Caching: Heavily cached external API calls to minimize latency and costs.
  • Lightweight & Fast: Bloat-free frontend design prioritizing UX and speed.
About
Your personal AI agent for sales,Jobs and other buisnesses made for zenith hackathon
expediteai.vercel.app/
Topics
langchainlanggraphtypescript
Resources
Readme
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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})();
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try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - BEASTSHRIRAM/OutboundAI: Your personal AI agent for sales,Jobs and other buisnesses made for zenith hackathon · GitHub
Skip to content
BranchesTags
Repository files navigation
EXPEDITE (OutboundAI)
StatusPRs WelcomeGDPR CompliantArchitectureTech Stack
EXPEDITE is an autonomous AI agent designed for B2B sales and recruiting outreach. It drastically reduces manual prospect research by utilizing advanced LLM pipelines (LangGraph) to find verified leads, extract insights, and draft hyper-personalized outreach emails.
Built for scale, speed, and real-world ROI.
Methodology
EXPEDITE operates on an Evidence-First Pipeline. Unlike traditional scraping wrappers, EXPEDITE leverages agentic orchestration to ensure every prospect is verified and every drafted email contains personalized, highly relevant context.
  1. Intent & Location Scoping: The user defines an objective (e.g., "Find Series A fintechs") and an optional location (e.g., "San Francisco"). The agent translates this into targeted API queries.
  2. Parallel Agent Execution: Using a LangGraph state machine (ScoutAgent), the system orchestrates sub-tasks. It searches Hunter.io and Apollo for domain contacts, pulling recent news and company intelligence concurrently.
  3. Data Verification (Proof Ledger): Every lead is subjected to a deliverability check (MX records, SMTP checks) and recorded in a transparent Proof Ledger.
  4. Contextual Drafting: Instead of generic templates, the LLM uses the gathered company intelligence and location context to write personalized drafts designed to cut through the noise.
  5. ROI Analytics: The platform strictly tracks output, actively visualizing the hours saved, leads found, and emails drafted on the main dashboard.
Architecture & Flow Diagram
The application is built on a split architecture: a lightweight React/Vite frontend and a robust, async-first FastAPI backend.
graph TD
%% Frontend Components
subgraph Frontend ["Frontend (React + Vite)"]
UI[Launchpad UI] --> ApiClient[API Client]
Dashboard[ROI Dashboard] --> ApiClient
end
%% Backend Components
subgraph Backend ["Backend (FastAPI)"]
Router[Missions Router]
Agent[ScoutAgent]
LLM[LLM Service]
Integrations[Integration Layer]
ApiClient -->|POST /missions| Router
Router --> Agent
Agent <--> LLM
Agent --> Integrations
end
%% External Services
subgraph External ["External Services"]
Hunter[Hunter.io API]
Apollo[Apollo API]
WebScraper[Firecrawl / Web]
Integrations --> Hunter
Integrations --> Apollo
Integrations --> WebScraper
end
%% Database
subgraph DB ["Database"]
Mongo[(MongoDB)]
Router --> Mongo
Agent --> Mongo
end

Loading
Getting Started
Prerequisites
  • Python 3.12+ (managed via uv)
  • Node.js v18+
  • MongoDB instance (Cloud or Local)
1. Backend Setup
Navigate to the backend directory and set up the environment:
cd backend
# Install dependencies using uv
uv sync
# Configure your environment
cp .env.example .env
# Fill in OPENAI_API_KEY, HUNTER_API_KEY, MONGODB_URI, etc.# Run the FastAPI server
uv run uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
2. Frontend Setup
Navigate to the frontend directory:
cd frontend
# Install dependencies
npm install
# Run the development server
npm run dev
Security & Privacy (Trust Center)
EXPEDITE was built with enterprise-grade security in mind:
  • GDPR Compliant: Designed with data minimization principles.
  • Isolated Execution: User data is processed in isolated execution environments.
  • Zero Raw Passwords: Strict enforcement against storing raw passwords; robust auth via Clerk.
Contributing
We love open-source contributions! If you're interested in helping us build EXPEDITE, please check out our Contributing Guidelines for details on how to set up your local environment, navigate the codebase, and submit Pull Requests.
Key Features
  • ROI Analytics Dashboard: Real-time visibility into manual hours saved and leads verified.
  • Location-Specific Targeting: Hyper-local prospect searching directly from the Launchpad.
  • Intelligent Caching: Heavily cached external API calls to minimize latency and costs.
  • Lightweight & Fast: Bloat-free frontend design prioritizing UX and speed.
About
Your personal AI agent for sales,Jobs and other buisnesses made for zenith hackathon
expediteai.vercel.app/
Topics
langchainlanggraphtypescript
Resources
Readme
Contributing
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, 'i');
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GitHub - BEASTSHRIRAM/OutboundAI: Your personal AI agent for sales,Jobs and other buisnesses made for zenith hackathon · GitHub
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EXPEDITE (OutboundAI)
StatusPRs WelcomeGDPR CompliantArchitectureTech Stack
EXPEDITE is an autonomous AI agent designed for B2B sales and recruiting outreach. It drastically reduces manual prospect research by utilizing advanced LLM pipelines (LangGraph) to find verified leads, extract insights, and draft hyper-personalized outreach emails.
Built for scale, speed, and real-world ROI.
Methodology
EXPEDITE operates on an Evidence-First Pipeline. Unlike traditional scraping wrappers, EXPEDITE leverages agentic orchestration to ensure every prospect is verified and every drafted email contains personalized, highly relevant context.
  1. Intent & Location Scoping: The user defines an objective (e.g., "Find Series A fintechs") and an optional location (e.g., "San Francisco"). The agent translates this into targeted API queries.
  2. Parallel Agent Execution: Using a LangGraph state machine (ScoutAgent), the system orchestrates sub-tasks. It searches Hunter.io and Apollo for domain contacts, pulling recent news and company intelligence concurrently.
  3. Data Verification (Proof Ledger): Every lead is subjected to a deliverability check (MX records, SMTP checks) and recorded in a transparent Proof Ledger.
  4. Contextual Drafting: Instead of generic templates, the LLM uses the gathered company intelligence and location context to write personalized drafts designed to cut through the noise.
  5. ROI Analytics: The platform strictly tracks output, actively visualizing the hours saved, leads found, and emails drafted on the main dashboard.
Architecture & Flow Diagram
The application is built on a split architecture: a lightweight React/Vite frontend and a robust, async-first FastAPI backend.
graph TD
%% Frontend Components
subgraph Frontend ["Frontend (React + Vite)"]
UI[Launchpad UI] --> ApiClient[API Client]
Dashboard[ROI Dashboard] --> ApiClient
end
%% Backend Components
subgraph Backend ["Backend (FastAPI)"]
Router[Missions Router]
Agent[ScoutAgent]
LLM[LLM Service]
Integrations[Integration Layer]
ApiClient -->|POST /missions| Router
Router --> Agent
Agent <--> LLM
Agent --> Integrations
end
%% External Services
subgraph External ["External Services"]
Hunter[Hunter.io API]
Apollo[Apollo API]
WebScraper[Firecrawl / Web]
Integrations --> Hunter
Integrations --> Apollo
Integrations --> WebScraper
end
%% Database
subgraph DB ["Database"]
Mongo[(MongoDB)]
Router --> Mongo
Agent --> Mongo
end

Loading
Getting Started
Prerequisites
  • Python 3.12+ (managed via uv)
  • Node.js v18+
  • MongoDB instance (Cloud or Local)
1. Backend Setup
Navigate to the backend directory and set up the environment:
cd backend
# Install dependencies using uv
uv sync
# Configure your environment
cp .env.example .env
# Fill in OPENAI_API_KEY, HUNTER_API_KEY, MONGODB_URI, etc.# Run the FastAPI server
uv run uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
2. Frontend Setup
Navigate to the frontend directory:
cd frontend
# Install dependencies
npm install
# Run the development server
npm run dev
Security & Privacy (Trust Center)
EXPEDITE was built with enterprise-grade security in mind:
  • GDPR Compliant: Designed with data minimization principles.
  • Isolated Execution: User data is processed in isolated execution environments.
  • Zero Raw Passwords: Strict enforcement against storing raw passwords; robust auth via Clerk.
Contributing
We love open-source contributions! If you're interested in helping us build EXPEDITE, please check out our Contributing Guidelines for details on how to set up your local environment, navigate the codebase, and submit Pull Requests.
Key Features
  • ROI Analytics Dashboard: Real-time visibility into manual hours saved and leads verified.
  • Location-Specific Targeting: Hyper-local prospect searching directly from the Launchpad.
  • Intelligent Caching: Heavily cached external API calls to minimize latency and costs.
  • Lightweight & Fast: Bloat-free frontend design prioritizing UX and speed.
About
Your personal AI agent for sales,Jobs and other buisnesses made for zenith hackathon
expediteai.vercel.app/
Topics
langchainlanggraphtypescript
Resources
Readme
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - BEASTSHRIRAM/OutboundAI: Your personal AI agent for sales,Jobs and other buisnesses made for zenith hackathon · GitHub
Skip to content
BranchesTags
Repository files navigation
EXPEDITE (OutboundAI)
StatusPRs WelcomeGDPR CompliantArchitectureTech Stack
EXPEDITE is an autonomous AI agent designed for B2B sales and recruiting outreach. It drastically reduces manual prospect research by utilizing advanced LLM pipelines (LangGraph) to find verified leads, extract insights, and draft hyper-personalized outreach emails.
Built for scale, speed, and real-world ROI.
Methodology
EXPEDITE operates on an Evidence-First Pipeline. Unlike traditional scraping wrappers, EXPEDITE leverages agentic orchestration to ensure every prospect is verified and every drafted email contains personalized, highly relevant context.
  1. Intent & Location Scoping: The user defines an objective (e.g., "Find Series A fintechs") and an optional location (e.g., "San Francisco"). The agent translates this into targeted API queries.
  2. Parallel Agent Execution: Using a LangGraph state machine (ScoutAgent), the system orchestrates sub-tasks. It searches Hunter.io and Apollo for domain contacts, pulling recent news and company intelligence concurrently.
  3. Data Verification (Proof Ledger): Every lead is subjected to a deliverability check (MX records, SMTP checks) and recorded in a transparent Proof Ledger.
  4. Contextual Drafting: Instead of generic templates, the LLM uses the gathered company intelligence and location context to write personalized drafts designed to cut through the noise.
  5. ROI Analytics: The platform strictly tracks output, actively visualizing the hours saved, leads found, and emails drafted on the main dashboard.
Architecture & Flow Diagram
The application is built on a split architecture: a lightweight React/Vite frontend and a robust, async-first FastAPI backend.
graph TD
%% Frontend Components
subgraph Frontend ["Frontend (React + Vite)"]
UI[Launchpad UI] --> ApiClient[API Client]
Dashboard[ROI Dashboard] --> ApiClient
end
%% Backend Components
subgraph Backend ["Backend (FastAPI)"]
Router[Missions Router]
Agent[ScoutAgent]
LLM[LLM Service]
Integrations[Integration Layer]
ApiClient -->|POST /missions| Router
Router --> Agent
Agent <--> LLM
Agent --> Integrations
end
%% External Services
subgraph External ["External Services"]
Hunter[Hunter.io API]
Apollo[Apollo API]
WebScraper[Firecrawl / Web]
Integrations --> Hunter
Integrations --> Apollo
Integrations --> WebScraper
end
%% Database
subgraph DB ["Database"]
Mongo[(MongoDB)]
Router --> Mongo
Agent --> Mongo
end

Loading
Getting Started
Prerequisites
  • Python 3.12+ (managed via uv)
  • Node.js v18+
  • MongoDB instance (Cloud or Local)
1. Backend Setup
Navigate to the backend directory and set up the environment:
cd backend
# Install dependencies using uv
uv sync
# Configure your environment
cp .env.example .env
# Fill in OPENAI_API_KEY, HUNTER_API_KEY, MONGODB_URI, etc.# Run the FastAPI server
uv run uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
2. Frontend Setup
Navigate to the frontend directory:
cd frontend
# Install dependencies
npm install
# Run the development server
npm run dev
Security & Privacy (Trust Center)
EXPEDITE was built with enterprise-grade security in mind:
  • GDPR Compliant: Designed with data minimization principles.
  • Isolated Execution: User data is processed in isolated execution environments.
  • Zero Raw Passwords: Strict enforcement against storing raw passwords; robust auth via Clerk.
Contributing
We love open-source contributions! If you're interested in helping us build EXPEDITE, please check out our Contributing Guidelines for details on how to set up your local environment, navigate the codebase, and submit Pull Requests.
Key Features
  • ROI Analytics Dashboard: Real-time visibility into manual hours saved and leads verified.
  • Location-Specific Targeting: Hyper-local prospect searching directly from the Launchpad.
  • Intelligent Caching: Heavily cached external API calls to minimize latency and costs.
  • Lightweight & Fast: Bloat-free frontend design prioritizing UX and speed.
About
Your personal AI agent for sales,Jobs and other buisnesses made for zenith hackathon
expediteai.vercel.app/
Topics
langchainlanggraphtypescript
Resources
Readme
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
GitHub - BEASTSHRIRAM/OutboundAI: Your personal AI agent for sales,Jobs and other buisnesses made for zenith hackathon · GitHub
Skip to content
BranchesTags
Repository files navigation
EXPEDITE (OutboundAI)
StatusPRs WelcomeGDPR CompliantArchitectureTech Stack
EXPEDITE is an autonomous AI agent designed for B2B sales and recruiting outreach. It drastically reduces manual prospect research by utilizing advanced LLM pipelines (LangGraph) to find verified leads, extract insights, and draft hyper-personalized outreach emails.
Built for scale, speed, and real-world ROI.
Methodology
EXPEDITE operates on an Evidence-First Pipeline. Unlike traditional scraping wrappers, EXPEDITE leverages agentic orchestration to ensure every prospect is verified and every drafted email contains personalized, highly relevant context.
  1. Intent & Location Scoping: The user defines an objective (e.g., "Find Series A fintechs") and an optional location (e.g., "San Francisco"). The agent translates this into targeted API queries.
  2. Parallel Agent Execution: Using a LangGraph state machine (ScoutAgent), the system orchestrates sub-tasks. It searches Hunter.io and Apollo for domain contacts, pulling recent news and company intelligence concurrently.
  3. Data Verification (Proof Ledger): Every lead is subjected to a deliverability check (MX records, SMTP checks) and recorded in a transparent Proof Ledger.
  4. Contextual Drafting: Instead of generic templates, the LLM uses the gathered company intelligence and location context to write personalized drafts designed to cut through the noise.
  5. ROI Analytics: The platform strictly tracks output, actively visualizing the hours saved, leads found, and emails drafted on the main dashboard.
Architecture & Flow Diagram
The application is built on a split architecture: a lightweight React/Vite frontend and a robust, async-first FastAPI backend.
graph TD
%% Frontend Components
subgraph Frontend ["Frontend (React + Vite)"]
UI[Launchpad UI] --> ApiClient[API Client]
Dashboard[ROI Dashboard] --> ApiClient
end
%% Backend Components
subgraph Backend ["Backend (FastAPI)"]
Router[Missions Router]
Agent[ScoutAgent]
LLM[LLM Service]
Integrations[Integration Layer]
ApiClient -->|POST /missions| Router
Router --> Agent
Agent <--> LLM
Agent --> Integrations
end
%% External Services
subgraph External ["External Services"]
Hunter[Hunter.io API]
Apollo[Apollo API]
WebScraper[Firecrawl / Web]
Integrations --> Hunter
Integrations --> Apollo
Integrations --> WebScraper
end
%% Database
subgraph DB ["Database"]
Mongo[(MongoDB)]
Router --> Mongo
Agent --> Mongo
end

Loading
Getting Started
Prerequisites
  • Python 3.12+ (managed via uv)
  • Node.js v18+
  • MongoDB instance (Cloud or Local)
1. Backend Setup
Navigate to the backend directory and set up the environment:
cd backend
# Install dependencies using uv
uv sync
# Configure your environment
cp .env.example .env
# Fill in OPENAI_API_KEY, HUNTER_API_KEY, MONGODB_URI, etc.# Run the FastAPI server
uv run uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
2. Frontend Setup
Navigate to the frontend directory:
cd frontend
# Install dependencies
npm install
# Run the development server
npm run dev
Security & Privacy (Trust Center)
EXPEDITE was built with enterprise-grade security in mind:
  • GDPR Compliant: Designed with data minimization principles.
  • Isolated Execution: User data is processed in isolated execution environments.
  • Zero Raw Passwords: Strict enforcement against storing raw passwords; robust auth via Clerk.
Contributing
We love open-source contributions! If you're interested in helping us build EXPEDITE, please check out our Contributing Guidelines for details on how to set up your local environment, navigate the codebase, and submit Pull Requests.
Key Features
  • ROI Analytics Dashboard: Real-time visibility into manual hours saved and leads verified.
  • Location-Specific Targeting: Hyper-local prospect searching directly from the Launchpad.
  • Intelligent Caching: Heavily cached external API calls to minimize latency and costs.
  • Lightweight & Fast: Bloat-free frontend design prioritizing UX and speed.
About
Your personal AI agent for sales,Jobs and other buisnesses made for zenith hackathon
expediteai.vercel.app/
Topics
langchainlanggraphtypescript
Resources
Readme
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages